• CDI Practical User Guides
  • I PREFACE
  • Welcome to the Machine Learning Domain
    • 🚀 What You’ll Gain
  • II DATA PREP & EDA
  • 1 How do you load and inspect a dataset for modeling?
    • 1.1 Recommended Dataset: Titanic Survival (Classification)
    • 1.2 Explanation
    • 1.3 Python Code
    • 1.4 R Code
  • 2 How do you handle missing values in a machine learning dataset?
    • 2.1 Explanation
    • 2.2 Python Code
    • 2.3 R Code
  • 3 How do you encode categorical variables for machine learning?
    • 3.1 Explanation
    • 3.2 Python Code
    • 3.3 R Code
  • 4 How do you split a dataset into training and testing sets?
    • 4.1 Explanation
    • 4.2 Python Code
    • 4.3 R Code
  • III SUPERVISED LEARNING MODEL TRAINING
  • 🧠 Supervised Learning
  • 5 How do you train and visualize a polynomial regression model using the Boston housing dataset?
    • 5.1 Explanation
    • 5.2 Python Code
    • 5.3 R Code
  • 6 How do you evaluate regression models using R², RMSE, and MAE?
    • 6.1 Explanation
    • 6.2 Python Code
    • 6.3 R Code
  • 7 How do you train a decision tree classifier?
    • 7.1 Explanation
    • 7.2 Python Code
  • 8 How do you evaluate model performance using a confusion matrix and accuracy?
    • 8.1 Explanation
    • 8.2 Python Code
  • 9 How do you evaluate a model using ROC curve and AUC?
    • 9.1 Explanation
    • 9.2 Python Code
    • 9.3 R Code
  • 10 How do you train a logistic regression model?
    • 10.1 Explanation
    • 10.2 Python Code
    • 10.3 R Code
  • 11 How do you train a random forest model and check variable importance?
    • 11.1 Explanation
    • 11.2 Python Code
    • 11.3 R Code
  • 12 How do you train a support vector machine (SVM) model?
    • 12.1 Explanation
    • 12.2 Python Code
    • 12.3 R Code
  • 13 How do you train a k-nearest neighbors (KNN) model?
    • 13.1 Explanation
    • 13.2 Python Code
    • 13.3 R Code
  • 14 How do you train a Naive Bayes model?
    • 14.1 Explanation
    • 14.2 Python Code
    • 14.3 R Code
  • 15 How do you train a gradient boosting model using XGBoost?
    • 15.1 Explanation
    • 15.2 Python Code
    • 15.3 R Code
  • 16 How do you visualize decision boundaries and understand model overfitting?
    • 16.1 Explanation
    • 16.2 Python Code
    • 16.3 R Code
  • 17 How do you compare L1 and L2 regularization in regression models?
    • 17.1 Explanation
    • 17.2 Python Code
    • 17.3 R Code
  • 18 How do you visualize L1 vs. L2 regularization paths side by side in R?
    • 18.1 Explanation
    • 18.2 R Code
  • IV UNSUPERVISED LEARNING MODEL TRAINING
  • 🔍 Unsupervised Learning
  • 19 How do you perform clustering with k-means?
    • 19.1 Explanation
    • 19.2 Recommended Dataset: Gene Expression (Unlabeled Clustering)
    • 19.3 Python Code
    • 19.4 R Code
  • 20 How do you reduce dimensions with PCA or t-SNE for visualization?
    • 20.1 Explanation
    • 20.2 Python Code
    • 20.3 R Code
  • 21 How do you cluster data using hierarchical clustering or DBSCAN?
    • 21.1 Explanation
    • 21.2 Python Code
    • 21.3 R Code
  • 22 How do you visualize clusters with UMAP in Python or R?
    • 22.1 Explanation
    • 22.2 Python Code
    • 22.3 R Code
  • 23 How do you combine dimensionality reduction with clustering to improve results?
    • 23.1 Explanation
    • 23.2 Python Code
    • 23.3 R Code
  • 24 How do you evaluate clustering quality using silhouette score and ARI?
    • 24.1 Explanation
    • 24.2 Python Code
    • 24.3 R Code
  • V MODEL COMPARISON
  • 🔍 Model Comparison
  • 25 How do you compare multiple models and choose the best one?
    • 25.1 Explanation
    • 25.2 Python Code
    • 25.3 R Code
  • 26 How do you create a heatmap to compare model performance across metrics?
    • 26.1 Explanation
    • 26.2 Python Code
    • 26.3 R Code
  • VI FEATURE IMPORTANCE
  • 27 How do you tune hyperparameters to improve model performance?
    • 27.1 Explanation
    • 27.2 Python Code
    • 27.3 R Coce
  • VII MODEL INTERPRETATION
  • 28 How do you explain predictions using SHAP or LIME?
    • 28.1 Explanation
    • 28.2 Python Code
    • 28.3 R Coce
  • VIII MODE DEPLOYMENT
  • 29 How do you save and load machine learning models for reuse?
    • 29.1 Explanation
    • 29.2 Python Code
    • 29.3 R Code
  • 30 How do you build a basic Streamlit app to deploy your ML model?
    • 30.1 Explanation
    • 30.2 Python Code: streamlit_app.py
  • Explore More Guides

Machine Learning Q&A Guide

Machine Learning Q&A Guide


Last updated: July 16, 2025